Development of vehicle maneuvering system for autonomous driving

Recently, autonomous driving technology has received significant interest, and consequently, various autonomous driving algorithms have been developed by researchers. An experimental (conventional) vehicle verifies the strategy for autonomous driving by testing the performance and feasibility of the corresponding algorithms. Implementation of the autonomous driving strategy in the conventional vehicle system requires modification of the electronic control unit (ECU); however, this is limited by security issues and the high cost of development.In this paper, a maneuvering system that can control the steering angle, braking force, and accelerating signal so that autonomous driving does not operate via the ECU is proposed. Because the steering angle is an important factor in terms of the vehicle motion behavior, a robust angle controller based on the disturbance observer is adopted. Moreover, to ensure safety and user convenience, a user intention recognition algorithm is proposed. Based on this algorithm, the maneuvering system can be disabled during control without using any perception sensor. Finally, several experiments are conducted to evaluate the functions of the maneuvering system. A scenario-based driving test is also performed to verify the feasibility of the maneuvering system.     

Efficient graph attentional network for 3D object detection from Frustum-based LiDAR point clouds

LiDAR-based 3D object detection is important for autonomous driving scene perception, but point clouds produced by LiDAR are irregular and unstructured in nature, and cannot be adopted by the conventional Convolutional Neural Networks (CNN). Recently, Graph Convolutional Networks (GCN) has been proved as an ideal way to handle non-Euclidean structure data, as well as for point cloud processing. However, GCN involves massive computation for searching adjacent nodes, and the heavy computational cost limits its applications in processing large-scale LiDAR point cloud in autonomous driving. In this work, we adopt a frustum-based point cloud-image fusion scheme to reduce the amount of LiDAR point clouds, thus making the GCN-based large-scale LiDAR point clouds feature learning feasible. On this basis, we propose an efficient graph attentional network to accomplish the goal of 3D object detection in autonomous driving, which can learn features from raw LiDAR point cloud directly without any conversions. We evaluate the model on the public KITTI benchmark dataset, the 3D detection mAP is 63.72% on KITTI Cars, Pedestrian and Cyclists, and the inference speed achieves 7.9 fps on a single GPU, which is faster than other methods of the same type.     

车联网信息对驾驶员视觉注意与行为的影响

目的探讨不同驾驶情景下车联网信息呈现形式及复杂度对驾驶过程和绩效的影响。方法采用3(信息复杂度:无、简单、复杂)*2(信息通道:视、听)*3(驾驶场景:纵向、横向、转角)多因素混合设计,以驾驶员视觉注意指标、驾驶行为指标为因变量,共招募60名驾驶员,分两组。结果 (1)车联网信息以听觉通道呈现会增加行车稳定性及方向盘控制稳定性,且不会损害驾驶员的视觉注意行为,视觉则相反;(2)信息简单也会增加行车稳定性及方向盘控制稳定性。结论横向与纵向路径以听觉呈现简单信息有利于促进安全驾驶行为,转角路径中应避免车联网信息。     

轻音乐对驾驶疲劳的双向影响

目的本实验探索驾驶负荷累积与轻音乐对驾驶疲劳的影响。方法采用2(驾驶负荷累积)×2(音乐条件)×3(模拟驾驶阶段)混合设计,对32名驾驶员被试进行模拟驾驶实验,采集驾驶员驾驶速度和他们的垂直搜索广度与瞳孔直径。结果第45-60 min时,对于无驾驶负荷累积的被试,轻音乐组车速显著快于无音乐组,轻音乐组垂直搜索广度显著大于无音乐组;对于有驾驶负荷累积的被试,轻音乐组的瞳孔直径小于无音乐组(边缘显著),轻音乐组的车速慢于无音乐组。结论轻音乐对无累积疲劳的驾驶员具有唤醒作用,加快他们的车速,扩大垂直搜索广度,对驾驶绩效具有积极的影响;轻音乐对有累积疲劳的驾驶员会产生催眠作用,缩小瞳孔直径,车速减慢,对驾驶绩效具有消极的影响。     

Construction of Asymmetrical Hexameric Biomimetic Motors with Continuous Single‐Directional Motion by Sequential Coordination

The significance of bionanomotors in nanotechnology is analogous to mechanical motors in daily life. Here the principle and approach for designing and constructing biomimetic nanomotors with continuous single‐directional motion are reported. This bionanomotor is composed of a dodecameric protein channel, a six‐pRNA ring, and an ATPase hexamer. Based on recent elucidations of the one‐way revolving mechanisms of the phi29 double‐stranded DNA (dsDNA) motor, various RNA and protein elements are designed and tested by single‐molecule imaging and biochemical assays, with which the motor with active components has been constructed. The motor motion direction is controlled by three operation elements: (1) Asymmetrical ATPase with ATP‐interacting domains for alternative DNA binding/pushing regulated by an arginine finger in a sequential action manner. The arginine finger bridges two adjacent ATPase subunits into a non‐covalent dimer, resulting in an asymmetrical hexameric complex containing one dimer and four monomers. (2) The dsDNA translocation channel as a one‐way valve. (3) The hexameric pRNA ring geared with left‐/right‐handed loops. Assessments of these constructs reveal that one inactive subunit of pRNA/ATPase is sufficient to completely block motor function (defined as K = 1), implying that these components work sequentially based on the principle of binomial distribution and Yang Hui's triangle.     

A primary influence vertex approach to identify driving factors in complex integrated agri-industrial systems – an example from sugarcane supply and processing systems

Integrated agri-industrial systems (IAISs), such as sugarcane supply and processing systems, are complex systems and hence generally difficult to understand and manage. The large number factors in IAISs coupled with the complex interrelationships among the factors make it challenging to identify the points of intervention for improving their overall performance. Several approaches, such as the network theory and the Theory of Constraints have been used to identify important factors in systems with variations in success. This paper demonstrates a primary influence vertex approach for identifying and ranking the factors that drive the performance of IAISs. The approach is based on comprehensive causal network analyses and was tested in four relatively diverse large-scale sugarcane milling operations in South Africa. Results from the analyses were found to be consistent with the literature and external knowledge of the milling areas as at the time of the study. It is concluded that the approach can proffer a sound basis from which deeper rooted problems in systems can be identified on an ongoing basis. It is, however, recommended that the approach should be systematically compared with other relevant methods that are used to analyse complex systems.     

基于CFD的新型风幕控尘装置的模拟研究及其应用

针对传统风幕控尘装置设备多、空间小、掘进机移动不便等诸多不足之处,⌒进行了优化设计。为确保新型风幕控尘装置的实际应用效果,指导现场防尘系统设计,确定抽出式风机风筒安设位置,依(山西某矿掘进巷道工作面1:1建立了物理几何模型,并利用计算流体动力学(CFD)进行模拟。根(湍流模型特性及流体运动特点,选取拉格朗日法离散相模型对掘进巷道粉尘浓度分布进行了模拟分析并进行现场实践。结果显示,所选物理几何模型参数设置合理,拟合精度达到预测要求;加装新型风幕控尘装置前后,粉尘浓度下降对比明显;距底板2.1 m处安设抽出式风机风筒时,系统降尘效果显著。     

变频调速技术在刨煤机成套开采系统中的应用

介绍了刨煤机成套开采系统的组成及变频调速技术在该系统中的应用。变频调整技术的应用提高了传动系统的调速效率,使得传动系统能够实现无级调速,降低了刨煤机成套开采系统在启动时传动系统承受的振动载荷,解决了刨头牵引电动机因频繁启动、换向而发热的缺陷,实现了真正意义上的"软启动",提高了刨煤机成套开采系统的传动可靠性和工作安全性。     

案例驱动下数据结构教材的编写

介绍数据结构的基本概念,讨论对数据结构课程的认知,提出教材编写过程中的几个要点,强调案例驱动教学模式在教材编写中的重要性,从而形成教材自身的特色。